Microscale Systems Research Articles

Droplet generation in a co-flowing microchannel under the non-uniform magnetic fields produced by an electric current loop

Micro-magnetofluidics, the study of fluid behavior under magnetic fields in microscale systems, is vital for applications like drug delivery, chemical synthesis, and lab-on-a-chip technologies. Controlling droplet size and formation frequency in these systems is challenging due to the complex interplay of magnetic forces and fluid dynamics. This study introduces a novel approach to control droplet generation in a co-flowing microchannel under a non-uniform magnetic field generated by an electric current loop. Critical parameters such as electric current intensity, continuous phase flow rate, and current loop position are systematically examined for their impact on droplet behavior. The results highlight the unique influence of the magnetic field configuration, specifically the electric current loop, in inducing a transition from dripping to jetting flow patterns with increasing current intensity, leading to larger droplets and reduced generation frequency. Additionally, a distinct behavior of droplet coalescence near the current loop, followed by re-separation, is observed when the loop is positioned downstream of the inlet. Moreover, increasing the continuous phase flow rate consistently reduced droplet size and increased generation frequency, regardless of the current loop’s position

Using Adjoint-Based Forecast Sensitivity to Observation to Evaluate a Wind Profiler Data Assimilation Strategy and the Impact of Data on Short-Term Forecasts

A wind profiler radar detects fine spatiotemporal resolution dynamical information, enabling the capture of meso- and micro-scale systems. Experience gained from observing system experiments (OSEs) studies confirms that reasonable profiler assimilation techniques can achieve improved short-term forecasts. This study further applies the adjoint-based forecast sensitivity to observation (FSO) method to investigate the quantitative impact of a profiler data assimilation strategy on short-term forecasts, and the results are consistent with those obtained from OSEs, further demonstrating that FSO and OSEs can be used to evaluate the effect of data assimilation techniques from different perspectives. Considering the unique advantage that the FSO can quantify the interactions between various observing systems and the impact on improving the model forecasts according to specific needs without costly additional calculations, we further diagnose in detail the observation impacts from multiple perspectives, including the observation platform, observation variables, and spatial distribution. And the results show that dynamical variables are more significant in improving forecasts compared to the other observed variables. Meanwhile, the dense profiler observations resulted in a more significant impact when radiosonde observations were not detected. The upper-level single winds monitored by profiler radars play a more important role in improving forecast skill. The FSO method measures the impact of an individual observing system, which can be used to enrich the evaluation of data assimilation schemes, efficiently calculate the impacts of multisource observations, and contribute to future development in adaptive observation, observation quality control, and observation error optimization.

Optimal micro-pump-storage system for the average Greek hotel with PV generation

In this paper, a modern micro-scale pumped hydro storage system that is specifically designed to operate in coordination with photovoltaics installed at the roof of an average Greek hotel is presented. The fictitious hotel chosen as a case study, displays the energy profile of an average sea-side hotel around the Mediterranean Sea, while photovoltaics’ energy generation is assumed to follow the typical production profile of such sites. Pumped hydro storage and photovoltaic generation, size and cost have been appropriately modeled so that they realistically simulate their operational scheme, while also considering the spatial and technical characteristics and limitations of such projects. Results derived from the implementation of such a scheme into the Average Greek hotel demonstrated significant monetary benefits, accompanied with a substantial net annual profit and low payback period of the investment.

Cell-membrane coated nanoparticles: Role of machine learning and applications in diagnosis and therapy

The advancement of biomedical technologies has introduced a promising approach for targeted drug delivery—utilizing nano and microscale systems. These compounds provide the potential for precise drug delivery to specific tissues, enhancing diagnostic accuracy and therapeutic efficacy, thereby minimizing off-target effects and improving site-specific accumulation. They enable controlled release of therapeutic agents, reducing the systemic toxicity often associated with conventional drugs. This review particularly highlights the critical role of cell membrane-decorated nanocompounds (CDCs), which represent a significant innovation in this field. Artificial Intelligence (AI) plays a pivotal role in enhancing the precision of CDCs, utilizing advanced algorithms to predict optimal delivery routes and release mechanisms. Moreover, AI-driven analytics are instrumental in the real-time monitoring and adjustment of drug release rates, ensuring maximum efficacy and patient safety. This review delves into the applications of CDCs, exploring their characterization, drug delivery potential, and future prospects, with a specific focus on how cell membrane coatings can revolutionize the landscape of drug delivery. Furthermore, the integration of AI in these processes is emphasized, particularly in how it can significantly impact future therapeutic strategies.

Three-Dimensionally Printed Microsystems to Facilitate Flow-Based Study of Cells from Neurovascular Barriers of the Retina.

Rapid prototyping has produced accessible manufacturing methods that offer faster and more cost-effective ways to develop microscale systems for cellular testing. Commercial 3D printers are now increasingly adapted for soft lithography, where elastomers are used in tandem with 3D-printed substrates to produce in vitro cell assays. Newfound abilities to prototype cellular systems have begun to expand fundamental bioengineering research in the visual system to complement tissue engineering studies reliant upon complex microtechnology. This project used 3D printing to develop elastomeric devices that examined the responses of retinal cells to flow. Our experiments fabricated molds for elastomers using metal milling, resin stereolithography, and fused deposition modeling via plastic 3D printing. The systems were connected to flow pumps to simulate different flow conditions and examined phenotypic responses of endothelial and neural cells significant to neurovascular barriers of the retina. The results indicated that microdevices produced using 3D-printed methods demonstrated differences in cell survival and morphology in response to external flow that are significant to barrier tissue function. Modern 3D printing technology shows great potential for the rapid production and testing of retinal cell responses that will contribute to both our understanding of fundamental cell response and the development of new therapies. Future studies will incorporate varied flow stimuli as well as different extracellular matrices and expanded subsets of retinal cells.

Nutritional load in post-prandial oxidative stress and the pathogeneses of diabetes mellitus

Diabetes mellitus affected more than 500 million of people globally, with an annual mortality of 1.5 million directly attributable to diabetic complications. Oxidative stress, in particularly in post-prandial state, plays a vital role in the pathogenesis of the diabetic complications. However, oxidative status marker is generally poorly characterized and their mechanisms of action are not well understood. In this work, we proposed a new framework for deep characterization of oxidative stress in erythrocytes (and in urine) using home-built micro-scale NMR system. The dynamic of post-prandial oxidative status (against a wide variety of nutritional load) in individual was assessed based on the proposed oxidative status of the red blood cells, with respect to the traditional risk-factors such as urinary isoprostane, reveals new insights into our understanding of diabetes. This new method can be potentially important in drafting guidelines for sub-stratification of diabetes mellitus for clinical care and management.

Free-standing microscale photonic lantern spatial mode (De-)multiplexer fabricated using 3D nanoprinting

Photonic lantern (PL) spatial multiplexers show great promise for a range of applications, such as future high-capacity mode division multiplexing (MDM) optical communication networks and free-space optical communication. They enable efficient conversion between multiple single-mode (SM) sources and a multimode (MM) waveguide of the same dimension. PL multiplexers operate by facilitating adiabatic transitions between the SM arrayed space and the single MM space. However, current fabrication methods are forcing the size of these devices to multi-millimeters, making integration with micro-scale photonic systems quite challenging. The advent of 3D micro and nano printing techniques enables the fabrication of freestanding photonic structures with a high refractive index contrast (photopolymer-air). In this work we present the design, fabrication, and characterization of a 6-mode mixing, 375 µm long PL that enables the conversion between six single-mode inputs and a single six-mode waveguide. The PL was designed using a genetic algorithm based inverse design approach and fabricated directly on a 7-core fiber using a commercial two-photon polymerization-based 3D printer and a photopolymer. Although the waveguides exhibit high index contrast, low insertion loss (−2.6 dB), polarization dependent (−0.2 dB) and mode dependent loss (−4.4 dB) were measured.

DNA-Based Replication of Programmable Colloidal Assemblies.

Nature uses replication to amplify the information necessary for the intricate structures vital for life. Despite some successes with pure nucleotide structures, constructing synthetic microscale systems capable of replication remains largely out of reach. Here, a functioning strategy is shown for the replication of microscale particle assemblies using DNA-coated colloids. By positioning DNA-functionalized colloids using capillary forces and embedding them into a polymer layer, programmable sequences of patchy particles are created that act as a primer and offer precise binding of complementary particles from suspension. The strings of complementary colloids are cross-linked, released from the primer, and purified via flow cytometric sorting to achieve a purity of up to 81% of the replicated sequences. The replication of strings of up to five colloids and non-linear shapes is demonstrated with particles of different sizes and materials. Furthermore, a pathway for exponential self-replication is outlined, including preliminary data that shows the transfer of patches and binding of a second-generation of assemblies fromsuspension.

On designing a wavy sinusoidal micromixer for efficient mixing of viscoelastic fluids harnessing elastic instability and elastic turbulence phenomena

Effective mixing in microscale systems encounters difficulties due to the inherently low Reynolds numbers. This investigation delves into harnessing elastic instability and elastic turbulence phenomena to improve the mixing performance of viscoelastic fluids within a wavy sinusoidal micromixer with variable cross-sectional areas. Previous experiments demonstrated enhanced mixing efficiency, while our numerical simulations indicate that this enhancement is true up to a certain Weissenberg number. Beyond this threshold, a further increase in the Weissenberg number results in diminished mixing efficiency. Moreover, we observe that mixing efficiency increases with the Deborah number, albeit with an exponential increase in pressure drop. Conversely, mixing efficiency also increases with the number of turns in the micromixer, albeit with a linear increase in pressure drop. Hence, selecting a wavy micromixer with more turns and a relatively lower Deborah number is advisable for achieving comparable efficiency with reduced pressure drop. Additionally, the shear-thinning properties of viscoelastic fluids impede mixing efficiency by suppressing elastic instability and elastic turbulence phenomena. In conclusion, this study provides valuable insights for designing optimal wavy micromixers that effectively mix viscoelastic fluids by utilizing elastic instability and elastic turbulence phenomena.

Accurate and efficient multiscale simulation of a heterogeneous elastic beam via computation on small sparse patches

Modern `smart' materials have complex microscale structure, often with unknown macroscale closure. The Equation-Free Patch Scheme empowers us to non-intrusively, efficiently, and accurately simulate over large scales through computations on only small well-separated patches of the microscale system. Here the microscale system is a solid beam of random heterogeneous elasticity. The continuing challenge is to compute the given physics on just the microscale patches, and couple the patches across un-simulated macroscale space, in order to establish efficiency, accuracy, consistency, and stability on the macroscale. Dynamical systems theory supports the scheme. This research program is to develop a systematic non-intrusive approach, both computationally and analytically proven, to model and compute accurately macroscale system levels of general complex physical and engineering systems.

Molecular simulation-guided and physics-informed constitutive modeling of highly stretchable hydrogels with dynamic ionic bonds

Adaptive polymers are being designed with dynamic molecular bonds or chain interactions to respond with external stimuli with unparalleled mechanical properties and multifunctionality. An elegant example is to substantially enhance the stretchability and toughness of hydrogels through the use of ionic bond interactions. To assist the materials design and applications, a predictive theory is in high demand. However, existing multi-scale mechanics models often rely on empirical assumptions and relationships derived from polymer chemistry or physics to describe the evolution of microscale details under external stimuli, which are challenging to be validated experimentally. This study introduces a new methodology to develop constitutive theories for stretchable hydrogels based on insights garnered from molecular dynamics (MD) simulations. The continuum-level viscoelastic theory establishes the thermodynamics framework for stress-strain relationships, while MD simulations inform the evolution mechanisms of microscale bond interactions and network rearrangements, such as the bond distance and network relaxation time. These insights are then properly formulated based on polymer physics principles and fed into the continuum-level model. The resulting constitutive theory closely captures the stress responses at various loading conditions observed in experiments, as well as the microscale system volume and bond distance uncovered in MD simulations. Parametric studies are conducted to investigate the influences of various loading and material parameters on the mechanical properties of the materials, including loading rates, network crosslinking density, maximum strain, and bonding strength. Overall, the study establishes the connection between microscale network structure and mechanical responses of stretchable hydrogels with dynamic ionic bonds. It also offers practical guidance for optimizing material structures and loading conditions to enhance energy absorption and dissipation capabilities. The modeling approach can be extended to the study of other adaptive polymers with different dynamic bonds to create more precise and physically meaningful constitutive models.

Assessment of pDNA isoforms using microfluidic electrophoresis.

The recent rise in nucleic acid-based vaccines and therapies has resulted in an increased demand for plasmid DNA (pDNA). As a result, there is added pressure to streamline the manufacturing of these vectors, particularly their design and construction, which is currently considered a bottleneck. A significant challenge in optimizing pDNA production is the lack of high-throughput and rapid analytical methods to support the numerous samples produced during the iterative plasmid construction step and for batch-to-batch purity monitoring. pDNA is generally present as one of three isoforms: supercoiled, linear, or open circular. Depending on the ultimate use, the desired isoform may be supercoiled in the initial stages for cell transfection or linear in the case of mRNA synthesis. Here, we present a high-throughput microfluidic electrophoresis method capable of detecting the three pDNA isoforms and determining the size and concentration of the predominant supercoiled and linear isoforms from 2 to 7kb. The limit of detection of the method is 0.1ng/µL for the supercoiled and linear isoforms and 0.5ng/µL for the open circular isoform, with a maximum loading capacity of 10-15ng/µL. The turnaround time is 1min/sample, and the volume requirement is 10µL, making the method suitable for process optimization and batch-to-batch analysis. The results presented in this study will enhance the understanding of electrophoretic transport in microscale systems dependent on molecular conformations and potentially aid technological advances in diverse areas relevant to microfluidic devices.

Dynamic interactions in MHD Jeffrey fluid: Exploring peristalsis, electro osmosis and homogeneous/heterogeneous chemical reactions

This paper investigates the behavior of magnetohydrodynamic (MHD) peristaltic flow with electroosmosis. The Jeffrey fluid in microchannel under the influence of homogeneous-heterogeneous chemical reaction has been taken. The heat absorption and nonlinear radiation are also scrutinized here. This study contributes to the fundamental understanding of complex fluid dynamics in microscale systems and offer valuable insights for designing and optimizing microfluidic devices. In the framework of mathematical simulation, the relevant dimensional nonlinear equations are reduced into dimensionless equations by using linear transformations. Thus Debye-Hückel linearization has been employed. The streamline approach with mathematical modelling has been performed within the limits of δ≪1 and Re→0. The appropriate equations for temperature and concentration with boundary conditions are solved by using perturbation approach whereas, the exact solution is found for velocity equation. A graphical depiction of crucial physical characteristics on velocity, temperature, concentration and streamlines are reported in the last section. It is observed that the velocity distribution decreases for the higher value of M (0.5≤M≤2) however, it increases for the escalating values of Uhs (−0.5≤Uhs≤1.5). When Rn(0.1≤Rn≤0.6) gets stronger then temperature profile decreases. It is noticed that the concentration profile decays owing to enhancement in Sc(−0.5≤Sc≤1.5). As the M (0.5≤M≤2) increases, the size of trapped bolus decreases. Escalating values of temperature ratio parameterθw(1.0≤θw≤1.6)enhances the Nusselt number. The novelty of this study lies in the comprehensive analysis of multiple complex phenomena in a single framework. The interplay between MHD electroosmotic flow, homogeneous heterogeneous chemical reactions, peristalsis, nonlinear thermal radiation, heat absorption and no-slip condition has not been explored in previous research.

A discrete event approach to micro-scale traffic modeling in urban environment

In this work, we present a new approach based on a discrete event formalism to model and simulate micro-scale urban traffic systems. The formalism is a coupling between the P-DEVS (Parallel-Discrete Event System Specification) formalism and UML (Unified Modeling Language) state machines. A system is represented by a set of coupled components. Each component supports the dynamics and logic of a system element. The models presented include the streets, intersections and traffic signs, all of which can be synchronized together through specific mechanisms. These models can be applied to real-world OpenStreetMap networks. A discrete event-driven adaptation of the simplified Gipps car-following model is introduced, and subsequently compared to its discrete time counterpart. The results show that our discrete event model follows dynamics which are similar to those of a discrete time model with a low update time step of 0.1s, despite not taking certain non-linearities of the latter into account. In terms of vehicle state changes and computation time, our approach outperforms the discrete time one with an update time step of 1s, both on a simple case study and on a real network.

Efficient computational homogenisation of 2D beams of heterogeneous elasticity using the patch scheme

Modern ‘smart’ materials have complex heterogeneous microscale structure, often with unknown macroscale closure but one we need to realise for large scale engineering and science. The multiscale Equation-Free Patch Scheme empowers us to non-intrusively, efficiently, and accurately predict large scale, system level, solutions through computations on only small sparse patches of the given detailed microscale system. Here the microscale system is that of a 2D beam of heterogeneous elasticity, with either fixed–fixed, fixed–free, or periodic boundary conditions. We demonstrate that the described multiscale Patch Scheme simply, efficiently, and stably predicts the beam’s macroscale, with a controllable accuracy, at finite scale separation. Dynamical systems theory supports the scheme. This article points the way for others to use this innovative systematic non-intrusive approach, via a developing toolbox of functions, to model and compute accurately macroscale system-levels of general complex physical and engineering systems.

Elastoviscoplasticity intensifies the unstable flows through a micro-contraction geometry

This study focuses on the two-dimensional numerical investigation of complex fluid flows through a micro-contraction geometry in the creeping flow regime, specifically examining elastoviscoplastic (EVP) fluids. These fluids exhibit a combination of viscous, elastic, and plastic behaviors. The governing equations, including mass and momentum, are solved using a finite volume method-based discretization technique. Saramito’s constitutive model is utilized to accurately represent the viscous, elastic, and plastic responses of the EVP fluid. The present results demonstrate significant differences in flow dynamics, such as vortex dynamics and transitions between flow regimes (e.g., steady to unsteady), when compared to simple Newtonian and non-Newtonian viscoelastic (VE) or viscoplastic (VP) fluids. This study reveals that when the yield strain (ϵy) exceeds a critical value, approximately ranging from 0.79 to 0.89, the flow transits from a steady to an unsteady state for the EVP fluids. Importantly, the present study shows that EVP fluids exhibit intensified chaotic flow dynamics and increased instability compared to VE and VP fluids under similar flow conditions. However, the presence of shear-thinning behavior in EVP fluids suppresses this instability. The analysis of local velocity fields and flow deformation in this study highlights the impact on the stretching of fluid microstructure and elastic stresses, which ultimately contribute to the origin of this intensified unstable flow condition for EVP fluids. The finding from this study holds significant potential for enhancing heat or mass transfer rates and mixing efficiency in micro-scale systems, where the prevailing steady and laminar flow conditions often hinder these transport processes.

Stochastic bandgap optimization for multiscale elastic metamaterials with manufacturing imperfections

Bandgaps are endowed to elastic metamaterials (EMMs) attributed to the rationally designed unit cells and extensive works are devoted to bandgap enlargement for improving the applicability of EMMs in multi-disciplinary applications. Nonetheless, most existing optimization frameworks neglect manufacturing imperfections, such as microscale heterogeneity and system uncertainties, which can significantly affect bandgap behaviors. Without properly accounting for these effects, the design may fail to achieve the optimal goal, exhibiting consistently ultra-wide wave attenuation bands in practical EMMs. Herein, in this paper, a stochastic bandgap optimization framework is developed for EMMs involving manufacturing imperfections, aiming at optimizing the first two statistical moments of the normalized bandwidth (NB) simultaneously. To alleviate the large computational costs in approximating statistical moments, a surrogate model is employed to reveal the constitutive relationship between system parameters and NB for the multiscale EMM. Moreover, to solve the optimization problem effectively and efficiently, a high-order mutation strategy is proposed to develop an improved particle swarm optimization (PSO) variant, namely the adaptively high-order mutation-based PSO (AHMPSO). To demonstrate the viability and efficiency of the proposed framework, a numerical investigation is implemented on a 3D EMM, which highlights enlarged bandwidths coupling with an improvement in the robustness of optimum.

Experimental and Computational Evidence of Damped Axial Conduction with Reciprocating Flow

Abstract Axial conduction is a crucial performance deteriorating factor in miniaturized heat transfer devices, primarily due to the low fluid flow rates, high solid cross-sectional to free-flow area ratio, and use of high thermal conductivity materials. These causative factors, inherent to micro-scale systems, should be such that the axial conduction is minimum. The reciprocating flow of the convective fluid (instead of steady unidirectional flow) is proposed per se as an alternative, which directly alters the solid temperature profile, the root cause of axial conduction. An experimental setup is built as a proof of the concept. In the test rig, a double-acting reciprocating pump generates a fully reversing periodic flow of air through a flow channel carved into a steel block embedded with a heater. The experimental temperature profile in the solid at the cyclic steady state is bell-shaped, indicating a virtual adiabatic plane capable of restricting axial heat transfer. The experimental results are verified taking the help of an independent finite-element-based numerical analysis. Similarly, the non-dimensional interfacial flux ratio (?_0 ), integrally related to axial conduction, for unidirectional and reciprocating flow is significantly different. This ratio in the vicinity of the inlet is ~53% less with the reciprocating compared to the equivalent unidirectional flow. The optimal thermal performance with the reciprocating flow is correlated through a critical Strouhal number expression, Sr ≤ (pDh)/L. In thermal management applications employing reciprocating flow, the limiting relation can be used to determine flow parameters and optimum geometry design.

Heating and cooling are fundamentally asymmetric and evolve along distinct pathways

According to conventional wisdom, a system placed in an environment with a different temperature tends to relax to the temperature of the latter, mediated by the flows of heat or matter that are set solely by the temperature difference. It is becoming clear, however, that thermal relaxation is much more intricate when temperature changes push the system far from thermodynamic equilibrium. Here, by using an optically trapped colloidal particle, we show that microscale systems under such conditions heat up faster than they cool down. We find that between any pair of temperatures, heating is not only faster than cooling but the respective processes, in fact, evolve along fundamentally distinct pathways, which we explain with a new theoretical framework that we call thermal kinematics. Our results change the view of thermalization at the microscale and will have a strong impact on energy-conversion applications and thermal management of microscopic devices, particularly in the operation of Brownian heat engines.

Modeling of temperature convection in microtubes under point heating: dependence of convection velocity on tilt angle

This paper presents the results of a study of the dependence of the convection velocity of a liquid in a microtube on the angle of inclination during point heating. This study utilizes a previously prepared finite-volume mesh of a cone-shaped microtube for use within the OpenFOAM software package. A detailed analysis and description of the mathematical model and a series of computational experiments using the built-in buoyantBoussinesqPimpleFoam solver are performed. An important step in this paper is the post-processing of the results obtained from a series of computational experiments. The paper provides a detailed description of the features of the data processing carried out for direct quantitative comparison of the results obtained in the individual experiments of the series. In the conducted computational experiments, quantitative data were obtained to reveal the dependence of the liquid convection velocity in a microtube on the angle of inclination during spot heating. From the obtained results, a significant influence of the inclination angle on the convection velocity was revealed, which can serve as a solution for the problems of optimization of mixing processes in microscale systems in the future.